• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1737-1745
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• 2000

In the last decade, considerable research effort has been studied ot multi-dimensional GIS and many spatiotemporal operations and query languages have been proposed. Many of them have focused on designing of operation, which process with data that is valid at current point in time. But, research that studied to updata operation that updates or inserts the past data is very rare. This paper designs an update operation and an insertion operation for integrity of past data of multi-dimensional GIS, and propose a new data model for efficient processing of the operations.

• The Journal of the Korea Contents Association
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• v.2 no.2
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• pp.36-41
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• 2002

In a mediator based information integration system that integrates information distributed on various data sources as a common data model, there are many researches on a common data mode providing a reservation of integrated data semantic, and on a resolution for a conflict caused by integrating various data modes. This paper proposes the DataBlender system that minimizes the semantic losses and provides an easy resolution for a conflict using the XML technology like as XML Schema, XQuery. Futhermore, it provides an integrated query facility usable in the mobile environment. So it becomes a foundation for a next Internet business environment.

• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.1-8
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• 2005

Similar melody searching is an operation that finds such melodies similar to a given query melody from a music database. In this paper, we address the development of a system that detects plagiarism based on the similar melody searching. We first Propose a novel similarity model that supports alignment as well as shifting. Also, we suggest a method for indexing the features extracted from each melody, and a method for processing plagiarism detection by using the index. By our plagiarism detection system composers can easily searches for such melodies that are similar to their ones from music databases. Through performance evaluation via a series of experiments, we show the effectiveness of our approach. The results reveal that our approach outperforms the sequential-scan-based one in speed up to around 31 times.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.385-389
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• 2009

As a new community forming environment, the blog platform enables sharing of the resources in blogosphere through active information exchange. Many researches have been performed to recommend appropriate resources to users from vast amounts of blog resources. As one of the solutions OSEM defines the knowledge base in the blogosphere with ontology for effectively modeling it. In this paper, we propose a technique of converting the knowledge base into the OWL ontology for sharing it on the semantic web environment. An inference method is then applied to the OWL ontology for recommending interest blog communities. For this aim, a mapping method is offered and then SWRL inference and SPARQL query based on the ontology are employed to extract interest blog communities.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.6
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• pp.672-677
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• 2010

Recently, the efficient management system for large-scale sensor data has been required due to the increasing deployment of large-scale sensor networks. In this paper, we propose a cloud-based sensor data management system with low cast, high scalability, and efficiency. Sensor data in sensor networks are transmitted to the cloud through a cloud-gateway. At this point, outlier detection and event processing is performed. Transmitted sensor data are stored in the Hadoop HBase, distributed column-oriented database, and processed in parallel by query processing module designed as the MapReduce model. The proposed system can be work with the application of a variety of platforms, because processed results are provided through REST-based web service.

• Bulletin of the Korean Chemical Society
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• v.35 no.7
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• pp.2097-2108
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• 2014

Chemical feature based quantitative pharmacophore models were generated using the HypoGen module implemented in DS2.5. The best hypothesis, Hypo1, which was characterized by the highest correlation coefficient (0.96), the highest cost difference (61.60) and the lowest RMSD (0.74), consisted of one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic and one ring aromatic. The reliability of Hypo1 was validated on the basis of cost analysis, test set, Fischer's randomization method and GH test method. The validated Hypo1 was used as a 3D search query to identify novel inhibitors. The screened molecules were further refined by employing ADMET, docking studies and visual inspection. Three compounds with novel scaffolds were selected as the most promising candidates for the designing of Mcl-1 antagonists. Finally, a 10 ns molecular dynamics simulation was carried out on the complex of receptor and the retrieved ligand to demonstrate that the binding mode was stable during the MD simulation.

• Journal of Korean Library and Information Science Society
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• v.18
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• pp.91-138
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• 1991

The paper describes the application areas of the hypertext and the relevance of hypertext principles to the information retrieval system. As to the techniques of the hypertext information retrieval the various navigation method including a guided tour, a history list, a browser, a book-mark, etc. are discussed. The query system is considered as the other technique to be integrated into the hypertext system for the enhancement of the interactive function of the information retrieval. Based on the theoretical background, a conceptual model of hypertext information retrieval system was constructed using GUIDE which was developed by P.J. Brown of Kent University. 16 bibliographic records from LISA of 1991(June) were used for the illustration of the basic operation of the system. Though the study could not reach the implementation level due to the absolute constraints of the time and experimental environments, further efforts will continue to develop a prototype system of a hypertext information retrieval. A few conclusions can be derived from the study : (1) The integration of the hypertext into the information retrieval system can be justified by permitting the end-users to have much stronger and more flexible interaction with the system. (2) The more the degree of the sofistication of the existing information retrieval system is the more the possibility of the development of an effective and user-oriented information retrieval system will be greater by integrating guide as a front end system to the underlying software. (3) The deep knowledge about the functions of information retrieval which can be enhanced by the hypertext could be acquired by the information retrieval specialists.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.556-558
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• 2001

전체 영상을 이용하지 않고 영상 안에 포함된 특정 객체 혹은 영역만을 이용하는 "영역에 의한 질의(query-by-region)" 방법은 내용기반 영상 검색 중 상위개념의 방법 이지만, 영상 분할의 한계, 여러개로 분할된 영역을 모두 검색하기 위한 인덱싱 문제, 유사성 측정 시 선형적으로 분리되지 않는 특징 값들에 대한 무리한 선형 조합으로 인한 검색 오류와 같은 많은 문제점을 안고 있다. 따라서 본 논문에서는 영역 기반 영상 검색 시스템인 FRIP에 대하여 영상 분할의 한계를 극복하고, 사용자의 주관성을 영상 검색에 적용하기 위해 확률적 연관성 학습 모델(MPFRL)을 유사성 측정 단계에서 적용 하였고, 아울러 검색 모델로는 기존에 일반적으로 사용되어 오던, 선형 모델을 사용하지 않고 선형 모델보다 유연한 검색 결과를 보여주는 확률적 이접 부울 모델(PDB)을 사용하였다. 또한, 검색 시간을 단축 시키기 위해, 선형 검색 방법에 부울 AND 연산자를 적용 시킴으로써, 검색 시간을 상당부분 단축 할 수 있었다. 실험 결과, 본 논문에서 제안하는 방법(MPFRL+PDB)을 사용할 경우 검색 결과가 선형 조합 보다 향상되는 것을 알 수 있었다. 아울러 사용자 피드백을 통해 사용자가 특징 가중치를 일일이 조절하지 않더라도 단순한 몇 번의 클릭만으로 사용자의 주관성을 반영하고 보다 정확한 검색 결과를 보여 줄 수 있는 시스템을 설계 할 수 있었다.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.724-740
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• 2016

The performance issues of screening large database compounds and multiple query compounds in virtual screening highlight a common concern in Chemoinformatics applications. This study investigates these problems by choosing group fusion as a pilot model and presents efficient parallel solutions in parallel platforms, specifically, the multi-core architecture of CPU and many-core architecture of graphical processing unit (GPU). A study of sequential group fusion and a proposed design of parallel CUDA group fusion are presented in this paper. The design involves solving two important stages of group fusion, namely, similarity search and fusion (MAX rule), while addressing embarrassingly parallel and parallel reduction models. The sequential, optimized sequential and parallel OpenMP of group fusion were implemented and evaluated. The outcome of the analysis from these three different design approaches influenced the design of parallel CUDA version in order to optimize and achieve high computation intensity. The proposed parallel CUDA performed better than sequential and parallel OpenMP in terms of both execution time and speedup. The parallel CUDA was 5-10x faster than sequential and parallel OpenMP as both similarity search and fusion MAX stages had been CUDA-optimized.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.